Search Results - regression ((models algorithm) OR (((model algorithm) OR (modified algorithm))))

Statistical modeling via bootstrapping and weighted techniques based on variances by Wan Ahmad, Wan Muhamad Amir, Aleng, Nor Azlida, Ali, Z, Mohd Ibrahim, Mohamad Shafiq

“…This data will be applied to the multiple logistic regression algorithm and modified Bayesian logistic regression. …”
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LASSO-type estimations for threshold autoregressive and heteroscedastic time series models. by Muhammad Jaffri Mohd Nasir

“…Furthermore, our CGD algorithms are also capable of estimating the pure GARCH model, unlike any similar algorithm for the same model in the literature. …”
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Optimum grouping in a modified genetic algorithm for discrete-time, non-linear system identification by Jamaluddin, Hishamuddin, Abd. Samad, M. F., Ahmad, Robiah, Yaacob, M. S.

“…One of the strategies applied is the modified genetic algorithm which relies on, among other things, the separation of the population into groups where each group undergoes mutual recombination operations. …”
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Optimum grouping in a modified genetic algorithm for discrete-time, non-linear system identification by Jamaluddin, H., Samad, M. F. A., Ahmad, R., Yaacob, M. S.

“…One of the strategies applied is the modified genetic algorithm which relies on, among other things, the separation of the population into groups where each group undergoes mutual recombination operations. …”
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The potential of a novel support vector machine trained with modified mayfly optimization algorithm for streamflow prediction by Adnan R.M., Kisi O., Mostafa R.R., Ahmed A.N., El-Shafie A.

“…Balancing; Forecasting; Stream flow; Support vector machines; Exploitation and explorations; Machine learning models; Optimisations; Optimization algorithms; Prediction modelling; Simulated annealing integrated with mayfly optimization; Streamflow prediction; Support vector regression models; Support vector regressions; Support vectors machine; Simulated annealing; algorithm; mayfly; optimization; prediction; streamflow; support vector machine; Jhelum River…”

Structural Equation Modeling Algorithm and Its Application in Business Analytics by Sorooshian, Shahryar

“…Structural Equation Modeling (SEM) is a statistical-based multivariate modeling methods, Application of SEM is similar but more powerful than regression analysis; and number of scientists using SEM in their research is rupidly inereasing. …”
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Stochastic And Modified Sequent Peak Algorithm For Reservoir Planning Analysis Considering Performance Indices by Oskoui, Issa Saket

“…Subsequently, Auto-regressive lag one, AR(1), coupled with Valencia-Schaake (V-S) disaggregation model are applied to generate synthetic streamflow data. …”
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Modeling the properties of terminal blend crumb rubber modified bitumen with crosslinking additives by Jegatheesan N., Ibrahim M.R., Ahmed A.N., Koting S., El-Shafie A., Katman H.Y.B.

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Optimum grouping in a modified genetic algorithm for discrete-time, non-linear system identification by Abd Samad, Md Fahmi

“…One of the strategies applied is the modified genetic algorithm which relies on, among other things, the separation of the population into groups where each group undergoes mutual recombination operations. …”
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An Analytical Algorithm for Delphi Method for Consensus Building and Organizational Productivity by Abd Hamid, Zahidy, Noor Azlinna, Azizan, Sorooshian, Shahryar

“…Structural Equation Modeling (SEM) is a statistical-based multivariate modeling methods, Application of SEM is similar but more powerful than regression analysis; and number of scientists using SEM in their research is rupidly inereasing. …”
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Comparison between fuzzy bootstrap weighted multiple linear regression and multiple linear regression: a case study for oral cancer modelling by Mohd Ibrahim, Mohamad Shafiq, Wan Ahmad, Wan Muhamad Amir, Hasan, Ruhaya, Harun, Masitah Hayati

“…(MLR) is the most common type of linear regression analysis. Current technology advancement and increasing of development of the new or modified methodology building leads to the development of an alternative method for multiple linear regression model calculation. …”
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Interaction effect of process parameters and Pd-electrocatalyst in formic acid electro-oxidation for fuel cell applications: Implementing supervised machine learning algorithms by Hossain S.K.S., Ali S.S., Rushd S., Ayodele B.V., Cheng C.K.

Dynamic modelling of a flexible beam structure using feedforward neural networks for active vibration control by Tuan Abdul Rahman, Tuan Ahmad Zahidi, As'arry, Azizan, Abdul Jalil, Nawal Aswan, Raja Ahmad, Raja Mohd Kamil

“…The performance of modified SFS algorithm to train a nonlinear auto-regressive exogenous model (NARX) structure FNNs-based model of the system was then compared with its predecessor and with several well-known metaheuristic algorithms. …”
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Modeling and validation of base pressure for aerodynamic vehicles based on machine learning models by Quadros, Jaimon Dennis, Khan, Sher Afghan, Aabid, Abdul, Baig, Muneer

“…The data for training and testing the algorithms was derived using the regression equation developed using the Box-Behnken Design (BBD). …”
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Parallel algorithms for numerical simulations of EHD ion-drag micropump on distributed parallel computing systems by Shakeel Ahmed, Kamboh

“…The DPA-EHD is further modified by utilizing the pipelining parallelism to reduce the computing iterations and named as data parallel and pipelining algorithm (DPPA-EHD). …”
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Modification of the CREAMS Nutrient submodel by Saleh, Abdul Razak

“…The CREAMS nutrient submodel was modified to improve the prediction, of the nitrogen loss from a flat agricultural field with a fluctuating water table.The CREAMS nutrient submodal was modified by incorporating a water function in the CREAMS denitrification algorithm.The capability of the CREAMS nutrient submodel and modified CREAMS nutrient submodel in predicting nitrogen loss was evaluated by using linear regression analysis, t-test on the slope and intercept of the regression equation, standard deviation of differences, absolute average differences, and percent error.Observed data from an experimental plot near Baton Rouge, Louisiana, USA were used in this study.The modified model underestimated the total nitrogen losses by 2% compared to 35% overestimation by the CREAMS model.Overall performance of the modified model in predicting nitrogen losses was satisfactory.…”
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Standard equations for predicting the discharge coefficient of a modified high-performance side weir by Zaji, Amir Hossein, Bonakdari, Hossein, Shamshirband, Shahaboddin

“…Four different forms of the equations and two non-dimensional input combinations were used to develop the most appropriate model. The results obtained by our simple standard equations optimized by the PSO algorithm were compared with those of complex nonlinear regression equations, and our equations were more accurate in modeling the discharge coefficient. …”
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Modelling of biogas production process with evolutionary artificial neural network and genetic algorithm by Fakharudin, Abdul Sahli

“…The model output optimisation by genetic algorithm (GA) produces higher biogas production compared to the optimisation using statistical methods. …”
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Survival modelling, missing values and frailty with application to cervical cancer data / Nuradhiathy Abd Razak by Nuradhiathy, Abd Razak

“…This study also focuses on the test for detecting frailty in a positive stable Gompertz model. The Zhu’s score test (Zhu, 1998), modified score test and ln s based test (Sarker, 2002) may also be derived from such a model. …”
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A hybrid ART-GRNN online learning neural network with a ?-insensitive loss function by Yap K.S., Lim C.P., Abidin I.Z.

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